Dataset.std(on: str | List[str] | None = None, ddof: int = 1, ignore_nulls: bool = True) Any | Dict[str, Any][source]#

Compute the standard deviation of one or more columns.


This method uses Welford’s online method for an accumulator-style computation of the standard deviation. This method has numerical stability, and is computable in a single pass. This may give different (but more accurate) results than NumPy, Pandas, and sklearn, which use a less numerically stable two-pass algorithm. To learn more, see the Wikapedia article.


This operation will trigger execution of the lazy transformations performed on this dataset.


This operation requires all inputs to be materialized in object store for it to execute.


>>> import ray
>>> round(ray.data.range(100).std("id", ddof=0), 5)
>>> ray.data.from_items([
...     {"A": i, "B": i**2}
...     for i in range(100)
... ]).std(["A", "B"])
{'std(A)': 29.011491975882016, 'std(B)': 2968.1748039269296}
  • on – a column name or a list of column names to aggregate.

  • ddof – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.

  • ignore_nulls – Whether to ignore null values. If True, null values are ignored when computing the std; if False, when a null value is encountered, the output is None. This method considers np.nan, None, and pd.NaT to be null values. Default is True.


The standard deviation result.

For different values of on, the return varies:

  • on=None: an dict containing the column-wise std of all columns,

  • on="col": a scalar representing the std of all items in column "col",

  • on=["col_1", ..., "col_n"]: an n-column dict containing the column-wise std of the provided columns.

If the dataset is empty, all values are null. If ignore_nulls is False and any value is null, then the output is None.